Ship detection based on spatio-temporal features

The paper proposes the ship detection method based on Spatio-temporal Histograms of Oriented Gradients (STHOG) feature and Support Vector Machine (SVM). STHOG feature, which is the extension version of HOG feature, enables extract spatial and temporal features of an object. The ship detector based on HOG feature can wrongly detect the similar shape objects with ships. On the other hand, the ship detector based on STHOG feature can identify them successfully by utilizing temporal feature of an object. To extract temporal feature of an object, image registration is implemented and an image displacement by camera motion is corrected. Due to high dimensionality of STHOG feature, it requires high computational cost to scan entire image and find ship regions. Principal Component Analysis (PCA) is applied to STHOG feature to compress the dimension. In the computer simulations, the ship detection performance of the proposed method was evaluated. From the simulation results, our proposed method exhibited better results than ship detector based on PCA+HOG feature.

[1]  Rob G. J. Wijnhoven,et al.  Online learning for ship detection in maritime surveillance , 2010 .

[2]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[3]  James J. Little,et al.  Simultaneous Tracking and Action Recognition using the PCA-HOG Descriptor , 2006, The 3rd Canadian Conference on Computer and Robot Vision (CRV'06).

[4]  Chih-Jen Lin,et al.  Combining SVMs with Various Feature Selection Strategies , 2006, Feature Extraction.

[5]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[6]  Svitlana Zinger,et al.  Water region and multiple ship detection for port surveillance , 2012 .

[7]  Kamel Besbes,et al.  Automatic Remote-sensing Image Registration Using SURF , 2013 .

[8]  Dmitry B. Goldgof,et al.  Detection and tracking of ships in open sea with rapidly moving buoy-mounted camera system , 2012 .

[9]  Cordelia Schmid,et al.  A Spatio-Temporal Descriptor Based on 3D-Gradients , 2008, BMVC.

[10]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Takio Kurita,et al.  Selection of Histograms of Oriented Gradients Features for Pedestrian Detection , 2007, ICONIP.

[12]  Luca Iocchi,et al.  Camera based target recognition for maritime awareness , 2012, 2012 15th International Conference on Information Fusion.

[13]  Lionel Prevost,et al.  A Cascade of Boosted Generative and Discriminative Classifiers for Vehicle Detection , 2008, EURASIP J. Adv. Signal Process..